class_docstr: Augmenter that leverage contextual word embeddings to find top n similar word for augmentation.
  
  :param str model_path: Model name or model path. It used transformers to load the model. Tested 'facebook/bart-large-cnn',
      t5-small', 't5-base' and 't5-large'. For models, you can visit https://huggingface.co/models?filter=summarization
  :param int batch_size: Batch size.
  :param int min_length: The min length of output text.
  :param int max_length: The max length of output text. 
  :param float temperature: The value used to module the next token probabilities.
  :param int top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
  :param float top_p: If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or
      higher are kept for generation.
  :param str device: Default value is CPU. If value is CPU, it uses CPU for processing. If value is CUDA, it uses GPU
      for processing. Possible values include 'cuda' and 'cpu'. (May able to use other options)
  :param bool force_reload: Force reload the contextual word embeddings model to memory when initialize the class.
      Default value is False and suggesting to keep it as False if performance is the consideration.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.sentence as nas
  >>> aug = nas.AbstSummAug()
code: def __init__(
      self,
      model_path="t5-base",
      tokenizer_path="t5-base",
      min_length=20,
      max_length=50,
      batch_size=32,
      temperature=1.0,
      top_k=50,
      top_p=0.9,
      name="AbstSumm_Aug",
      device="cpu",
      force_reload=False,
      verbose=0,
      use_custom_api=True,
  ):
      super().__init__(
          action=Action.SUBSTITUTE,
          name=name,
          tokenizer=None,
          stopwords=None,
          device=device,
          include_detail=False,
          verbose=verbose,
      )
      self.model_path = model_path
      self.tokenizer_path = tokenizer_path
  
      self.model = self.get_model(
          model_path=model_path,
          tokenizer_path=tokenizer_path,
          device=device,
          force_reload=force_reload,
          min_length=min_length,
          max_length=max_length,
          batch_size=batch_size,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
          use_custom_api=use_custom_api,
      )
      self.device = self.model.device
  
file_path: nlpaug/nlpaug/augmenter/sentence/abst_summ.py
imports: import os
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.lang_models
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 1 to create a new AbstSummAug object from inherited base classes: SentenceAugmenter with example: obj = AbstSummAug(model_path, tokenizer_path, min_length, max_length, batch_size, temperature, top_k, top_p, name, device, force_reload, verbose, use_custom_api)